1. Field of the Invention
This invention relates to a controller for controlling an air conditioner, employing a neural network for learning different characteristics of the air conditioner and best controls the operating conditions such as superheat, performance, defrosting, etc., of the air conditioner according to a result of the learning. The invention particularly relates to an air conditioner controller that speedily brings the temperature of an air-conditioned room to a set temperature and stably maintains the set temperature according to a result of learning carried out through a neural network.
2. Description of the Prior Art
Control algorithms and control constants for an air conditioner are usually determined by laboratory tests.
FIG. 1 shows an example of a conventional controller for controlling an air conditioner. This controller employs a PID (proportional plus integral plus derivative) controller to control an object process (a refrigerating cycle) of the air conditioner. Control sequences and constants such as an integral time for the PID controller are determined according to laboratory tests.
In actual use of the air conditioner with the controller, environmental and operating conditions of the air conditioner frequently differ from those studied in laboratories. For example, the length of piping, the quantity of a coolant, and weather conditions involving rain, wind, and snow may not be equal to those used by laboratory tests. In this case, control constants obtained from the laboratory tests will be insufficient to properly control the air conditioner.
This problem will be explained with reference to controlling the operation frequency of a compressor of the air conditioner.
The compressor operation frequency is controlled to control the performance of the air conditioner, and a variable range of the operation frequency expands year by year. The conventional controller uniquely controls the frequency according to a difference between a room temperature and a set temperature.
FIGS. 2a to 2c show control modes of the conventional air conditioner controller. A light-load mode is for air-conditioning a sunlit south-facing room in warm seasons such as spring, or a properly insulated small room. The light-load mode widely changes the performance of the air conditioner in response to even a small change in load on the air conditioner. This results in repeatedly hunting a set temperature.
A heavy-load mode is for air-conditioning a poorly sunlit north-facing room in cold weather, or a badly insulated large room. The heavy-load mode narrowly changes the performance of the air conditioner in response to a change in load on the air conditioner, to take a long time to reach a set temperature. The room temperature slowly changes around the set temperature and is hardly stabilized.
Signals transmitted between an indoor unit and an outdoor unit of the air conditioner provide a limited quantity of information. With this limited information, the air conditioner controller must control the operation frequency of the compressor disposed in the outdoor unit. This is the reason why the conventional air conditioner controller, which uniquely uses a difference between a set temperature and a room temperature, is insufficient to deal with light or heavy load on the air conditioner.
Namely, the conventional air conditioner controller is unable to follow load fluctuations caused by seasonal changes or by the location of an air-conditioned room, and unstably hunts a set temperature or achieves a slow response.